Kevin Spiekermann

942 citations
13 papers · 658 indexed · h-index 10
Topics
Machine Learning in Materials Science (8 papers)Computational Drug Discovery Methods (5 papers)Advanced Chemical Physics Studies (4 papers)

In The Last Decade

Kevin Spiekermann

13 papers receiving 649 citations

Peers

Kevin Spiekermann
Comparison fields: 5 of 102
  • Biomedical Engineering 264
  • Materials Chemistry 223
  • Molecular Biology 188
  • Biomaterials 120
  • Computational Theory and Mathematics 112
Replace Bernhard Helk with:
Bernhard Helk Switzerland
Kayla G. Sprenger United States
Christian Luebbert Germany
Taehee Kang South Korea
Danny K. Chou United States
Derrick S. Katayama United States
Reza Esfandiary United States
Samir V. Jenkins United States
Tim J. Kamerzell United States
K. Sugiyama Japan
Kevin Spiekermann relative to Bernhard Helk Switzerland Bernhard Helk's profile →
Citations per field
00.5×
Bernhard Helk · 1×
Citations per year

Countries citing papers authored by Kevin Spiekermann

Since Specialization
Citations

This map shows the geographic impact of Kevin Spiekermann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin Spiekermann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Spiekermann more than expected).

Fields of papers citing papers by Kevin Spiekermann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Spiekermann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin Spiekermann. The network helps show where Kevin Spiekermann may publish in the future.

Co-authorship network of co-authors of Kevin Spiekermann

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Spiekermann. A scholar is included among the top collaborators of Kevin Spiekermann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kevin Spiekermann. Kevin Spiekermann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
#WorkIndexed citations
1 4
2 5
3 34
4 9
5 9
6 8
7 88
8 42
9 49
10 15
11 133
12 106
13 156

About Kevin Spiekermann

Kevin Spiekermann is a scholar working on Catalysis, Computational Theory and Mathematics and Materials Chemistry, having authored 13 papers that have together received 658 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (5 papers) and Advanced Chemical Physics Studies (4 papers). The work is most often cited by research in Biomaterials (120 citations), Microbiology (46 citations) and Computational Theory and Mathematics (112 citations). Kevin Spiekermann has collaborated with scholars based in United States, Germany and Belgium. Frequent co-authors include William H. Green, Weiwei Gao, Liangfang Zhang, Ronnie H. Fang, Pavimol Angsantikul, Lagnajit Pattanaik, Jia Zhuang, Yue Zhang, Jianhua Zhang and Wansong Chen. Their work appears in journals such as Journal of the American Chemical Society, Advanced Materials and The Journal of Physical Chemistry B.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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